XAI-KG: knowledge graph to support XAI and decision-making in manufacturing
Jo\v{z}e M. Ro\v{z}anec, Patrik Zajec, Klemen Kenda, Inna Novalija,, Bla\v{z} Fortuna, Dunja Mladeni\'c

TL;DR
This paper introduces XAI-KG, a domain-specific knowledge graph and ontology designed to enhance explainability and decision-making in manufacturing demand forecasting by collecting user feedback and improving model explanations.
Contribution
The paper presents a novel ontology and knowledge graph tailored for demand forecasting, enabling better assessment of explanation quality and decision support in manufacturing.
Findings
Validated on real-world manufacturing data
Improves understanding of forecast explanations
Supports feedback-driven model enhancement
Abstract
The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction. Such cues are of utmost importance to decision-making since they provide insights on the features that influenced most certain forecasts and let the user decide if the forecast can be trusted. Though many techniques were developed to explain black-box models, little research was done on assessing the quality of those explanations and their influence on decision-making. We propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions. This way, we provide means to improve forecasting models, explanations,…
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